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  • Open Access

Multilayer cycle benchmarking for high-accuracy error characterization

Alessio Calzona1,*, Miha Papič1,2, Pedro Figueroa-Romero1, and Adrian Auer1

  • *Contact author: alessio.calzona@meetiqm.com
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Phys. Rev. Research 8, 013111 – Published 30 January, 2026

DOI: https://doi.org/10.1103/4hbl-hpl3

Abstract

Accurate noise characterization is essential for reliable quantum computation. Effective Pauli noise models have emerged as powerful tools, offering a detailed description of the error processes with a manageable number of parameters, which guarantees the scalability of the characterization procedure. However, a fundamental limitation in the learnability of Pauli eigenvalues impedes a full high-accuracy characterization of both general and effective Pauli noise models, thereby restricting, e.g., the performance of noise-aware error mitigation techniques. We introduce multilayer cycle benchmarking (MLCB), an enhanced characterization protocol that improves the learnability associated with effective Pauli noise models by jointly analyzing multiple layers of Clifford gates. We show a simple experimental implementation and demonstrate that, in realistic scenarios, MLCB can reduce unlearnable noise degrees of freedom by up to 75%, improving the accuracy of sparse Pauli-Lindblad noise models and boosting the performance of error mitigation techniques like probabilistic error cancellation. Our results highlight MLCB as a scalable, practical tool for precise noise characterization and improved quantum computation.

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Phys. Rev. Research 8, 013111– Published 30 January, 2026
  • Received 18 January 2025
  • Accepted 4 December 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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